网格结构的神经网络优化与有限元数值模拟

IF 0.4 4区 物理与天体物理 Q4 PHYSICS, MULTIDISCIPLINARY
I. A. Podpruzhnikov, A. V. Vershinin, V. A. Levin, K. M. Zingerman
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引用次数: 0

摘要

研究了脉冲源产生的声波应用于由周期性晶格结构形成的线弹性固体的情况下所发生的影响。为了进行一系列基于有限元法的数值实验,在CAE Fidesys中模拟了各种类型的晶格结构。在这些点阵结构中,形成点阵结构的条的波动参数、施加脉冲的频率和测量隔声级的距离是不同的。形成晶格结构的细胞的一些点也是不同的。对其中一些可变参数进行了频率滤波。分析了变参数与隔声等级的关系。通过一系列的虚拟实验,建立了基于神经网络的隔声水平预测模型。该算法使用Python编程语言和scikit-learn库进行配置。该算法的使用使计算隔声级的时间减少了数百倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimization of lattice structures using neural networks and numerical simulations based on FEM

The effects occurring in the cases where the acoustic waves generated by a pulsed source are applied to linearly elastic solids formed by a periodic lattice structure are examined. For a series of numerical experiments based on the finite element method, various types of lattice structures are modeled in the CAE Fidesys. In these lattice structures, the parameters of the undulation of the bars forming the lattice structure, the frequency of the applied pulse, and the distance at which the sound insulation level is measured, are varied. A number of points forming the cells of the lattice structure are also varied. The presence of frequency filtering is established for some of these variable parameters. An analysis of the relationships of variable parameters and sound insulation level is made. Using a series of virtual experiments, a model predicting the level of sound insulation is built based on a neural network. This algorithm is configured using the Python programming language and the scikit-learn library. The use of this algorithm allows reduce the time for calculating the sound insulation level by a factor of hundreds.

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来源期刊
Russian Physics Journal
Russian Physics Journal PHYSICS, MULTIDISCIPLINARY-
CiteScore
1.00
自引率
50.00%
发文量
208
审稿时长
3-6 weeks
期刊介绍: Russian Physics Journal covers the broad spectrum of specialized research in applied physics, with emphasis on work with practical applications in solid-state physics, optics, and magnetism. Particularly interesting results are reported in connection with: electroluminescence and crystal phospors; semiconductors; phase transformations in solids; superconductivity; properties of thin films; and magnetomechanical phenomena.
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